Blockchain and Quantum FL-Based IDS for Security and Privacy of 6G-Enabled CE

Abstract

The rapid proliferation of 6G-enabled consumer electronics (CE) has introduced significant security and privacy challenges. Traditional security mechanisms often fall short in addressing these issues due to the unique characteristics of CE networks. To enhance intrusion detection systems (IDSs), data-driven artificial intelligence (AI) approaches have gained considerable attention. Nonetheless, AI-based IDSs face challenges related to scalability, privacy preservation, and high computational demands—particularly when handling high-dimensional and complex data. To overcome these limitations, this article proposes a novel framework called Blockchain and Quantum Federated Learning (BQFL). BQFL integrates blockchain technology and quantum computing (QC) with FL to provide an efficient, secure, robust, and privacy-preserving solution for intrusion detection in CE environments. Specifically, blockchain enables fault-tolerant and decentralized trust management for parameter aggregation on the quantum FL (QFL) server. Furthermore, the framework leverages the high-speed and low-latency capabilities of 6G networks to enable real-time and secure data processing and communication among a vast number of CE devices. We validate the effectiveness of the proposed framework through extensive experiments on the ToN-IoT dataset.